{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 0. Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pandas in c:\\users\\zengzhi\\anaconda3\\envs\\tensorflow\\lib\\site-packages (2.1.1)\n",
      "Requirement already satisfied: numpy>=1.23.2 in c:\\users\\zengzhi\\anaconda3\\envs\\tensorflow\\lib\\site-packages (from pandas) (1.26.0)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in c:\\users\\zengzhi\\anaconda3\\envs\\tensorflow\\lib\\site-packages (from pandas) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\zengzhi\\anaconda3\\envs\\tensorflow\\lib\\site-packages (from pandas) (2023.3.post1)\n",
      "Requirement already satisfied: tzdata>=2022.1 in c:\\users\\zengzhi\\anaconda3\\envs\\tensorflow\\lib\\site-packages (from pandas) (2023.3)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\zengzhi\\anaconda3\\envs\\tensorflow\\lib\\site-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n",
      "Requirement already satisfied: matplotlib in c:\\users\\zengzhi\\anaconda3\\envs\\tensorflow\\lib\\site-packages (3.8.0)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\zengzhi\\anaconda3\\envs\\tensorflow\\lib\\site-packages (from matplotlib) (1.1.1)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\zengzhi\\anaconda3\\envs\\tensorflow\\lib\\site-packages (from matplotlib) (0.12.0)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\zengzhi\\anaconda3\\envs\\tensorflow\\lib\\site-packages (from matplotlib) (4.43.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\zengzhi\\anaconda3\\envs\\tensorflow\\lib\\site-packages (from matplotlib) (1.4.5)\n",
      "Requirement already satisfied: numpy<2,>=1.21 in c:\\users\\zengzhi\\anaconda3\\envs\\tensorflow\\lib\\site-packages (from matplotlib) (1.26.0)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\users\\zengzhi\\anaconda3\\envs\\tensorflow\\lib\\site-packages (from matplotlib) (23.2)\n",
      "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\zengzhi\\anaconda3\\envs\\tensorflow\\lib\\site-packages (from matplotlib) (10.0.1)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\zengzhi\\anaconda3\\envs\\tensorflow\\lib\\site-packages (from matplotlib) (3.1.1)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\zengzhi\\anaconda3\\envs\\tensorflow\\lib\\site-packages (from matplotlib) (2.8.2)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\zengzhi\\anaconda3\\envs\\tensorflow\\lib\\site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install pandas\n",
    "%pip install matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import tensorflow.keras as keras\n",
    "from tensorflow.keras import backend as K\n",
    "from tensorflow.keras.layers import *\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy.io import loadmat\n",
    "from scipy.io import savemat\n",
    "import math\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "from matplotlib.ticker import LinearLocator\n",
    "\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy.random import seed\n",
    "seed(0)\n",
    "tf.random.set_seed(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Define Something in Advance\n",
    " x = x[0];\n",
    "    y = x[1];\n",
    "    uxy = x[2]; \n",
    "    dudx = x[3];\n",
    "    dudy = x[4];\n",
    "    du2dx2 = x[5];\n",
    "    du2dy2 = x[6];\n",
    "    du2dxdy = x[7];\n",
    "    laplace = x[8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def U(x,y):\n",
    "    return np.sin(x**2+y**2)       \n",
    "def dUdx(x,y):\n",
    "    return 2*x*np.cos(x**2+y**2)    \n",
    "def dUdy(x,y):\n",
    "    return 2*y*np.cos(x**2+y**2)   \n",
    "def d2Udx2(x,y):\n",
    "    return 2*np.cos(x**2+y**2)-4*x**2*np.sin(x**2+y**2)\n",
    "\n",
    "def d2Udy2(x,y):\n",
    "    return 2*np.cos(x**2+y**2)-4*y**2*np.sin(x**2+y**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Initialize the Grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "delta = 0.05\n",
    "x_domain = np.arange(0, 10, delta)\n",
    "y_domain = np.arange(0, 6, delta)\n",
    "x_mesh,y_mesh = np.meshgrid(x_domain, y_domain)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Define Physical Info."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    suppose: \n",
    "    x = x[0];\n",
    "    y = x[1];\n",
    "    uxy = x[2]; \n",
    "    dudx = x[3];\n",
    "    dudy = x[4];\n",
    "    du2dx2 = x[5];\n",
    "    du2dy2 = x[6];\n",
    "    du2dxdy = x[7];\n",
    "    laplace = x[8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "governing_equation_components = []\n",
    "governing_equation_components.append(Lambda(lambda x: tf.sin(x[2]**2)))\n",
    "governing_equation_components.append(Lambda(lambda x: x[8]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "governing_equation_mask = x_mesh*0+1\n",
    "governing_equation_mask[0,:]=0\n",
    "governing_equation_mask[-1,:]=0\n",
    "governing_equation_mask[:,0]=0\n",
    "governing_equation_mask[:,-1]=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "fx = governing_equation_mask*(np.sin(np.sin(x_mesh**2+y_mesh**2)**2) + 4*np.cos(x_mesh**2+y_mesh**2) - 4*(x_mesh**2+y_mesh**2)*np.sin(x_mesh**2+y_mesh**2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "estimate_equation_form = False\n",
    "equation_component_combination = [1.0,1.0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Define the Observations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    suppose: \n",
    "    x = x[0];\n",
    "    y = x[1];\n",
    "    uxy = x[2]; \n",
    "    dudx = x[3];\n",
    "    dudy = x[4];\n",
    "    du2dx2 = x[5];\n",
    "    du2dy2 = x[6];\n",
    "    du2dxdy = x[7];\n",
    "    laplace = x[8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "observation_components = []\n",
    "observation_components.append(Lambda(lambda x: x[2]))\n",
    "observation_components.append(Lambda(lambda x: x[3]))\n",
    "observation_components.append(Lambda(lambda x: x[4]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    format: [x,y,combination,value]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "observation_data = []\n",
    "# bottom\n",
    "for x in x_domain:\n",
    "    y = y_domain[0]\n",
    "    comb = [1,0,0]\n",
    "    v = np.sin(x**2+y**2)\n",
    "    observation_data.append([x,y,comb,v])\n",
    "# left\n",
    "for y in y_domain:\n",
    "    x = x_domain[0]\n",
    "    comb = [1,0,0]\n",
    "    v = np.sin(x**2+y**2)\n",
    "    observation_data.append([x,y,comb,v])  \n",
    "# up\n",
    "for x in x_domain:\n",
    "    y = y_domain[-1]\n",
    "    comb = [1,0,0]\n",
    "    v = np.sin(x**2+y**2)\n",
    "    observation_data.append([x,y,comb,v])   \n",
    "# right\n",
    "for y in y_domain:\n",
    "    x = x_domain[-1]\n",
    "    comb = [1,0,0]\n",
    "    v = np.sin(x**2+y**2)\n",
    "    observation_data.append([x,y,comb,v])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Define PICN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gradient_x_kernal_init(shape, dtype=tf.float32):\n",
    "    return tf.constant([[[[-1.0/delta]], [[1.0/delta]]]], dtype=dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gradient_x2_kernal_init(shape, dtype=tf.float32):\n",
    "    return tf.constant([[[[1.0/delta**2]], [[-2.0/delta**2]], [[1.0/delta**2]]]], dtype=dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gradient_y_kernal_init(shape, dtype=tf.float32):\n",
    "    return tf.constant([[[[-1.0/delta]]], [[[1.0/delta]]]], dtype=dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gradient_y2_kernal_init(shape, dtype=tf.float32):\n",
    "    return tf.constant([[[[1.0/delta**2]]], [[[-2.0/delta**2]]], [[[1.0/delta**2]]]], dtype=dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gradient_xy_kernal_init(shape, dtype=tf.float32):\n",
    "    return tf.constant([[[[1.0/delta**2]], [[-1.0/delta**2]]], [[[-1.0/delta**2]], [[1.0/delta**2]]]], dtype=dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def laplace_kernal_init(shape, dtype=tf.float32):\n",
    "    return tf.constant([[[[0]], [[1.0/delta**2]], [[0]]], [[[1.0/delta**2]], [[-4.0/delta**2]], [[1.0/delta**2]]], [[[0]], [[1.0/delta**2]], [[0]]]], dtype=dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs = [keras.layers.Input(shape=(1,1,1)),\n",
    "          keras.layers.Input(shape=(len(y_domain),len(x_domain),1)),\n",
    "          keras.layers.Input(shape=(len(y_domain),len(x_domain),1)),\n",
    "          keras.layers.Input(shape=(len(observation_data),len(observation_components)*4)),\n",
    "          keras.layers.Input(shape=(len(y_domain),len(x_domain),1))]\n",
    "\n",
    "hidden_field = keras.layers.Conv2DTranspose(filters=1, \n",
    "                                            kernel_size=[len(y_domain)+4,len(x_domain)+4], \n",
    "                                            activation='linear')(inputs[0])\n",
    "coordinates_x = inputs[1]\n",
    "coordinates_y = inputs[2]\n",
    "field = keras.layers.Conv2D(filters=1, \n",
    "                            kernel_size=3, \n",
    "                            padding='valid', \n",
    "                            activation=Lambda(lambda x: x+tf.tanh(x)))(hidden_field)\n",
    "gradient_x_field = keras.layers.Conv2D(filters=1, \n",
    "                                     kernel_size=[1,2], \n",
    "                                     padding='valid',\n",
    "                                     use_bias=False,\n",
    "                                     trainable=False,\n",
    "                                     kernel_initializer=gradient_x_kernal_init)(field)\n",
    "gradient_x2_field = keras.layers.Conv2D(filters=1, \n",
    "                                       kernel_size=[1,3], \n",
    "                                       padding='valid',\n",
    "                                       use_bias=False,\n",
    "                                       trainable=False,\n",
    "                                       kernel_initializer=gradient_x2_kernal_init)(field)\n",
    "gradient_y_field = keras.layers.Conv2D(filters=1, \n",
    "                                     kernel_size=[2,1], \n",
    "                                     padding='valid',\n",
    "                                     use_bias=False,\n",
    "                                     trainable=False,\n",
    "                                     kernel_initializer=gradient_y_kernal_init)(field)\n",
    "gradient_y2_field = keras.layers.Conv2D(filters=1, \n",
    "                                       kernel_size=[3,1], \n",
    "                                       padding='valid',\n",
    "                                       use_bias=False,\n",
    "                                       trainable=False,\n",
    "                                       kernel_initializer=gradient_y2_kernal_init)(field)\n",
    "gradient_xy_field = keras.layers.Conv2D(filters=1, \n",
    "                                       kernel_size=[2,2], \n",
    "                                       padding='valid',\n",
    "                                       use_bias=False,\n",
    "                                       trainable=False,\n",
    "                                       kernel_initializer=gradient_xy_kernal_init)(field)\n",
    "laplace_field = keras.layers.Conv2D(filters=1, \n",
    "                                       kernel_size=[3,3], \n",
    "                                       padding='valid',\n",
    "                                       use_bias=False,\n",
    "                                       trainable=False,\n",
    "                                       kernel_initializer=laplace_kernal_init)(field)\n",
    "phycial_fields = [coordinates_x,\n",
    "                  coordinates_y,\n",
    "                  field[:,1:-1,1:-1,:],\n",
    "                  gradient_x_field[:,1:-1,1:,:],\n",
    "                  gradient_y_field[:,1:,1:-1,:],\n",
    "                  gradient_x2_field[:,1:-1,:,:],\n",
    "                  gradient_y2_field[:,:,1:-1,:],\n",
    "                  gradient_xy_field[:,1:,1:,:],\n",
    "                  laplace_field]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "if estimate_equation_form==True:\n",
    "    tf_governing_equation_components = [component(phycial_fields) for component in governing_equation_components]\n",
    "    concat_equation_components = Lambda(lambda x: tf.concat(x,axis=-1))(tf_governing_equation_components)\n",
    "    governing_equation = keras.layers.Conv2D(filters=1, \n",
    "                                             kernel_size=[1,1], \n",
    "                                             padding='valid',\n",
    "                                             use_bias=False)(concat_equation_components)*inputs[4]\n",
    "else:\n",
    "    tf_weighted_governing_equation_components = [weight*component(phycial_fields) for [weight,component] in zip(equation_component_combination,governing_equation_components)]\n",
    "    concat_weighted_equation_components = Lambda(lambda x: tf.concat(x,axis=-1))(tf_weighted_governing_equation_components)\n",
    "    governing_equation = Lambda(lambda x: tf.reduce_sum(x,axis=-1,keepdims=True))(concat_weighted_equation_components)*inputs[4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf_observation_components = [component(phycial_fields) for component in observation_components]\n",
    "concat_observation_components = Lambda(lambda x: tf.concat(x,axis=-1))(tf_observation_components)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "left_x_position_index_list = []\n",
    "right_x_position_index_list = []\n",
    "left_x_position_weight_list = []\n",
    "right_x_position_weight_list = []\n",
    "\n",
    "bottom_y_position_index_list = []\n",
    "top_y_position_index_list = []\n",
    "bottom_y_position_weight_list = []\n",
    "top_y_position_weight_list = []\n",
    "\n",
    "\n",
    "for data in observation_data:\n",
    "    \n",
    "    left_x_position_index = int(np.floor((data[0] - x_domain[0])/delta))\n",
    "    right_x_position_index = left_x_position_index + 1\n",
    "    left_x_position_weight = 1-(data[0] - (x_domain[0]+delta*left_x_position_index))/delta\n",
    "    right_x_position_weight = 1-left_x_position_weight\n",
    "    \n",
    "    bottom_y_position_index = int(np.floor((data[1] - y_domain[0])/delta))\n",
    "    top_y_position_index = bottom_y_position_index + 1\n",
    "    bottom_y_position_weight = 1-(data[1] - (y_domain[0]+delta*bottom_y_position_index))/delta\n",
    "    top_y_position_weight = 1-bottom_y_position_weight\n",
    "    \n",
    "    if data[0] <= x_domain[0] + 1e-8:\n",
    "        left_x_position_index = 0\n",
    "        right_x_position_index = 1\n",
    "        left_x_position_weight = 1\n",
    "        right_x_position_weight = 0\n",
    "    if data[0] >= x_domain[-1] - 1e-8:\n",
    "        left_x_position_index = len(x_domain)-2\n",
    "        right_x_position_index = len(x_domain)-1\n",
    "        left_x_position_weight = 0\n",
    "        right_x_position_weight = 1\n",
    "    if data[1] <= y_domain[0] + 1e-8:\n",
    "        bottom_y_position_index = 0\n",
    "        top_y_position_index = 1\n",
    "        bottom_y_position_weight = 1\n",
    "        top_y_position_weight = 0\n",
    "    if data[1] >= y_domain[-1] - 1e-8:\n",
    "        bottom_y_position_index = len(y_domain)-2\n",
    "        top_y_position_index = len(y_domain)-1\n",
    "        bottom_y_position_weight = 0\n",
    "        top_y_position_weight = 1\n",
    "    \n",
    "    left_x_position_index_list.append(left_x_position_index)\n",
    "    right_x_position_index_list.append(right_x_position_index)\n",
    "    left_x_position_weight_list.append(left_x_position_weight)\n",
    "    right_x_position_weight_list.append(right_x_position_weight)\n",
    "\n",
    "    bottom_y_position_index_list.append(bottom_y_position_index)\n",
    "    top_y_position_index_list.append(top_y_position_index)\n",
    "    bottom_y_position_weight_list.append(bottom_y_position_weight)\n",
    "    top_y_position_weight_list.append(top_y_position_weight)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "left_bottom_indices = tf.constant([[0,y,x] for x,y in zip(left_x_position_index_list,bottom_y_position_index_list)])\n",
    "left_top_indices = tf.constant([[0,y,x] for x,y in zip(left_x_position_index_list,top_y_position_index_list)])\n",
    "right_bottom_indices = tf.constant([[0,y,x] for x,y in zip(right_x_position_index_list,bottom_y_position_index_list)])\n",
    "right_top_indices = tf.constant([[0,y,x] for x,y in zip(right_x_position_index_list,top_y_position_index_list)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "observation_data_left_bottom_part = Lambda(lambda x: tf.gather_nd(x,left_bottom_indices))(concat_observation_components)\n",
    "observation_data_left_top_part = Lambda(lambda x: tf.gather_nd(x,left_top_indices))(concat_observation_components)\n",
    "observation_data_right_bottom_part = Lambda(lambda x: tf.gather_nd(x,right_bottom_indices))(concat_observation_components)\n",
    "observation_data_right_top_part = Lambda(lambda x: tf.gather_nd(x,right_top_indices))(concat_observation_components)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "observation_data_four_part = Lambda(lambda x: tf.expand_dims(tf.concat(x,axis=-1),axis=0))([observation_data_left_bottom_part,observation_data_left_top_part,observation_data_right_bottom_part,observation_data_right_top_part])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "observation_data_interpolation_weights = np.expand_dims(\n",
    "np.asarray([np.concatenate([(np.asarray([observation_data[i][2][j] for j in range(len(observation_components))]))*left_x_position_weight_list[i]*bottom_y_position_weight_list[i],\n",
    " (np.asarray([observation_data[i][2][j] for j in range(len(observation_components))]))*left_x_position_weight_list[i]*top_y_position_weight_list[i],\n",
    " (np.asarray([observation_data[i][2][j] for j in range(len(observation_components))]))*right_x_position_weight_list[i]*bottom_y_position_weight_list[i],\n",
    " (np.asarray([observation_data[i][2][j] for j in range(len(observation_components))]))*right_x_position_weight_list[i]*top_y_position_weight_list[i]],axis=-1) for i in range(len(observation_data))]),\n",
    "    axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "observations = Lambda(lambda x: tf.reduce_sum(x[0]*x[1],axis=-1))([inputs[3][0,:,:],observation_data_four_part])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "pde_model = keras.Model(inputs=inputs[:3], outputs=phycial_fields)\n",
    "pde_model_train = keras.Model(inputs=inputs, outputs=[governing_equation,observations])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                Output Shape                 Param #   Connected to                  \n",
      "==================================================================================================\n",
      " input_1 (InputLayer)        [(None, 1, 1, 1)]            0         []                            \n",
      "                                                                                                  \n",
      " conv2d_transpose (Conv2DTr  (None, 124, 204, 1)          25297     ['input_1[0][0]']             \n",
      " anspose)                                                                                         \n",
      "                                                                                                  \n",
      " conv2d (Conv2D)             (None, 122, 202, 1)          10        ['conv2d_transpose[0][0]']    \n",
      "                                                                                                  \n",
      " conv2d_1 (Conv2D)           (None, 122, 201, 1)          2         ['conv2d[0][0]']              \n",
      "                                                                                                  \n",
      " conv2d_3 (Conv2D)           (None, 121, 202, 1)          2         ['conv2d[0][0]']              \n",
      "                                                                                                  \n",
      " conv2d_2 (Conv2D)           (None, 122, 200, 1)          3         ['conv2d[0][0]']              \n",
      "                                                                                                  \n",
      " conv2d_4 (Conv2D)           (None, 120, 202, 1)          3         ['conv2d[0][0]']              \n",
      "                                                                                                  \n",
      " conv2d_5 (Conv2D)           (None, 121, 201, 1)          4         ['conv2d[0][0]']              \n",
      "                                                                                                  \n",
      " input_2 (InputLayer)        [(None, 120, 200, 1)]        0         []                            \n",
      "                                                                                                  \n",
      " input_3 (InputLayer)        [(None, 120, 200, 1)]        0         []                            \n",
      "                                                                                                  \n",
      " tf.__operators__.getitem (  (None, 120, 200, 1)          0         ['conv2d[0][0]']              \n",
      " SlicingOpLambda)                                                                                 \n",
      "                                                                                                  \n",
      " tf.__operators__.getitem_1  (None, 120, 200, 1)          0         ['conv2d_1[0][0]']            \n",
      "  (SlicingOpLambda)                                                                               \n",
      "                                                                                                  \n",
      " tf.__operators__.getitem_2  (None, 120, 200, 1)          0         ['conv2d_3[0][0]']            \n",
      "  (SlicingOpLambda)                                                                               \n",
      "                                                                                                  \n",
      " tf.__operators__.getitem_3  (None, 120, 200, 1)          0         ['conv2d_2[0][0]']            \n",
      "  (SlicingOpLambda)                                                                               \n",
      "                                                                                                  \n",
      " tf.__operators__.getitem_4  (None, 120, 200, 1)          0         ['conv2d_4[0][0]']            \n",
      "  (SlicingOpLambda)                                                                               \n",
      "                                                                                                  \n",
      " tf.__operators__.getitem_5  (None, 120, 200, 1)          0         ['conv2d_5[0][0]']            \n",
      "  (SlicingOpLambda)                                                                               \n",
      "                                                                                                  \n",
      " conv2d_6 (Conv2D)           (None, 120, 200, 1)          9         ['conv2d[0][0]']              \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 25330 (98.95 KB)\n",
      "Trainable params: 25307 (98.86 KB)\n",
      "Non-trainable params: 23 (92.00 Byte)\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "def model_print_functoin(s):\n",
    "    with open('model_summary.txt','a') as f:\n",
    "        print(s, file=f)\n",
    "\n",
    "pde_model.summary(print_fn=model_print_functoin)\n",
    "pde_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Prepare the Training Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 1, 1, 1)\n"
     ]
    }
   ],
   "source": [
    "unit_constant = np.asarray([[[[1.0]]]],dtype=np.float32)\n",
    "training_input_data_0 = unit_constant\n",
    "print(training_input_data_0.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 120, 200, 1)\n"
     ]
    }
   ],
   "source": [
    "training_input_data_1 = np.expand_dims(x_mesh.astype(np.float32),axis=[0,-1])\n",
    "print(training_input_data_1.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 120, 200, 1)\n"
     ]
    }
   ],
   "source": [
    "training_input_data_2 = np.expand_dims(y_mesh.astype(np.float32),axis=[0,-1])\n",
    "print(training_input_data_2.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 640, 12)\n"
     ]
    }
   ],
   "source": [
    "training_input_data_3 = observation_data_interpolation_weights.astype(np.float32)\n",
    "print(training_input_data_3.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 120, 200, 1)\n"
     ]
    }
   ],
   "source": [
    "training_input_data_4 = np.expand_dims(governing_equation_mask.astype(np.float32),axis=[0,-1])\n",
    "print(training_input_data_2.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 120, 200, 1)\n"
     ]
    }
   ],
   "source": [
    "training_label_data_0 = np.expand_dims(fx.astype(np.float32),axis=[0,-1])\n",
    "print(training_label_data_0.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 640)\n"
     ]
    }
   ],
   "source": [
    "training_label_data_1 = np.expand_dims(np.asarray([data[3] for data in observation_data]),axis=[0])\n",
    "print(training_label_data_1.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_input_data = [training_input_data_0,training_input_data_1,training_input_data_2,training_input_data_3,training_input_data_4]\n",
    "training_label_data = [training_label_data_0,training_label_data_1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. Train the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['loss', 'tf.math.multiply_2_loss', 'lambda_14_loss']\n"
     ]
    }
   ],
   "source": [
    "pde_model_train.compile(optimizer=keras.optimizers.Adam(), loss=\"mse\")\n",
    "pde_model_train.save_weights('picn_initial_weights.h5')\n",
    "temp_history = pde_model_train.fit(x=training_input_data, y=training_label_data, epochs=1, verbose=0)\n",
    "history_keys = []\n",
    "for key in temp_history.history.keys():\n",
    "    history_keys.append(key)\n",
    "print(history_keys)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def record_predictions():\n",
    "    [_, _, uxy, dudx, dudy, d2udx2, d2udy2, d2udxdy, laplace] = pde_model.predict(training_input_data[:3])\n",
    "    uxy_list.append(uxy[:,:,:,0])\n",
    "    dudx_list.append(dudx[:,:,:,0])\n",
    "    dudy_list.append(dudy[:,:,:,0])\n",
    "    d2udx2_list.append(d2udx2[:,:,:,0])\n",
    "    d2udy2_list.append(d2udy2[:,:,:,0])\n",
    "    d2udxdy_list.append(d2udxdy[:,:,:,0])\n",
    "    laplace_list.append(laplace[:,:,:,0])\n",
    "\n",
    "class Per_X_Epoch_Record(tf.keras.callbacks.Callback):\n",
    "    def __init__(self, record_interval, verbose=1):\n",
    "        super(tf.keras.callbacks.Callback, self).__init__()\n",
    "        self.total_loss = history_keys[0]\n",
    "        self.domain_loss = history_keys[1]\n",
    "        self.bdc_loss = history_keys[2]\n",
    "        self.previous_total_loss = 9999999\n",
    "        self.record_interval = record_interval;\n",
    "        self.verbose = verbose\n",
    "\n",
    "    def on_epoch_end(self, epoch, logs={}):\n",
    "        \n",
    "        if epoch%self.record_interval == 0:\n",
    "            \n",
    "            current_total_loss = logs.get(self.total_loss)\n",
    "            current_domain_loss = logs.get(self.domain_loss)\n",
    "            current_bdc_loss = logs.get(self.bdc_loss)\n",
    "            \n",
    "            epoch_number_list.append(epoch)\n",
    "            total_loss_list.append(current_total_loss)\n",
    "            domain_loss_list.append(current_domain_loss)\n",
    "            boundary_loss_list.append(current_bdc_loss)\n",
    "        \n",
    "            if current_total_loss < self.previous_total_loss:\n",
    "                self.previous_total_loss = current_total_loss\n",
    "                pde_model_train.save_weights('picn_best_weights.h5')\n",
    "            \n",
    "            if self.verbose > 0:\n",
    "                print(\"epoch: {:10.5f} | total_loss: {:10.5f} | domain_loss: {:10.5f} | bdc_loss: {:10.5f}\".format(epoch,current_total_loss,current_domain_loss,current_bdc_loss))\n",
    "        \n",
    "            # evaluate the errors in f-domain\n",
    "            record_predictions()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "callbacks = [\n",
    "    Per_X_Epoch_Record(record_interval=100,verbose=1),\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch:    0.00000 | total_loss:   24.10568 | domain_loss: 23605.03906 | bdc_loss:    0.50115\n",
      "1/1 [==============================] - 0s 67ms/step\n",
      "epoch:  100.00000 | total_loss:    0.05096 | domain_loss:   50.17422 | bdc_loss:    0.00078\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch:  200.00000 | total_loss:    0.00843 | domain_loss:    8.36598 | bdc_loss:    0.00007\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch:  300.00000 | total_loss:    0.00308 | domain_loss:    3.05679 | bdc_loss:    0.00002\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch:  400.00000 | total_loss:    0.00157 | domain_loss:    1.56255 | bdc_loss:    0.00001\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch:  500.00000 | total_loss:    0.00095 | domain_loss:    0.94345 | bdc_loss:    0.00001\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch:  600.00000 | total_loss:    0.00062 | domain_loss:    0.61353 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch:  700.00000 | total_loss:    0.00041 | domain_loss:    0.41208 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch:  800.00000 | total_loss:    0.00028 | domain_loss:    0.28127 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch:  900.00000 | total_loss:    0.00019 | domain_loss:    0.19213 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 1000.00000 | total_loss:    0.00014 | domain_loss:    0.13828 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 1100.00000 | total_loss:    0.00010 | domain_loss:    0.09876 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 1200.00000 | total_loss:    0.00007 | domain_loss:    0.07395 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 1300.00000 | total_loss:    0.00010 | domain_loss:    0.09422 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 1400.00000 | total_loss:    0.00007 | domain_loss:    0.07161 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "epoch: 1500.00000 | total_loss:    0.00005 | domain_loss:    0.05364 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 1600.00000 | total_loss:    0.00028 | domain_loss:    0.27713 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 1700.00000 | total_loss:    0.00022 | domain_loss:    0.22242 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "epoch: 1800.00000 | total_loss:    0.00025 | domain_loss:    0.23840 | bdc_loss:    0.00001\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "epoch: 1900.00000 | total_loss:    0.00007 | domain_loss:    0.06821 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "epoch: 2000.00000 | total_loss:    0.00010 | domain_loss:    0.10480 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 16ms/step\n",
      "epoch: 2100.00000 | total_loss:    0.00057 | domain_loss:    0.56987 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 2200.00000 | total_loss:    0.00051 | domain_loss:    0.47906 | bdc_loss:    0.00003\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 2300.00000 | total_loss:    0.00025 | domain_loss:    0.25002 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 2400.00000 | total_loss:    0.00033 | domain_loss:    0.32695 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "epoch: 2500.00000 | total_loss:    0.00120 | domain_loss:    1.15923 | bdc_loss:    0.00004\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "epoch: 2600.00000 | total_loss:    0.00018 | domain_loss:    0.18107 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 2700.00000 | total_loss:    0.00034 | domain_loss:    0.33218 | bdc_loss:    0.00001\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 2800.00000 | total_loss:    0.00071 | domain_loss:    0.67270 | bdc_loss:    0.00004\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 2900.00000 | total_loss:    0.00077 | domain_loss:    0.77260 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 3000.00000 | total_loss:    0.00004 | domain_loss:    0.03760 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "epoch: 3100.00000 | total_loss:    0.00013 | domain_loss:    0.11500 | bdc_loss:    0.00002\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 3200.00000 | total_loss:    0.00010 | domain_loss:    0.09784 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 3300.00000 | total_loss:    0.00061 | domain_loss:    0.60848 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 3400.00000 | total_loss:    0.00031 | domain_loss:    0.31193 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "epoch: 3500.00000 | total_loss:    0.00080 | domain_loss:    0.79597 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 3600.00000 | total_loss:    0.00077 | domain_loss:    0.76590 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 3700.00000 | total_loss:    0.00005 | domain_loss:    0.04683 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 3800.00000 | total_loss:    0.00027 | domain_loss:    0.26969 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "epoch: 3900.00000 | total_loss:    0.00076 | domain_loss:    0.75827 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 4000.00000 | total_loss:    0.00027 | domain_loss:    0.26029 | bdc_loss:    0.00001\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 4100.00000 | total_loss:    0.00030 | domain_loss:    0.28118 | bdc_loss:    0.00002\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 4200.00000 | total_loss:    0.00045 | domain_loss:    0.43978 | bdc_loss:    0.00001\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 4300.00000 | total_loss:    0.00009 | domain_loss:    0.08770 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 4400.00000 | total_loss:    0.00012 | domain_loss:    0.11553 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "epoch: 4500.00000 | total_loss:    0.00068 | domain_loss:    0.67872 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 4600.00000 | total_loss:    0.00054 | domain_loss:    0.51369 | bdc_loss:    0.00003\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "epoch: 4700.00000 | total_loss:    0.00036 | domain_loss:    0.36345 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 4800.00000 | total_loss:    0.00072 | domain_loss:    0.72101 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 4900.00000 | total_loss:    0.00028 | domain_loss:    0.27828 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "Training time cost:   21.44741 seconds\n"
     ]
    }
   ],
   "source": [
    "epoch_number_list = []\n",
    "total_loss_list = []\n",
    "domain_loss_list = []\n",
    "boundary_loss_list = []\n",
    "uxy_list = []\n",
    "dudx_list = []\n",
    "dudy_list = []\n",
    "d2udx2_list = []\n",
    "d2udy2_list = []\n",
    "d2udxdy_list = []\n",
    "laplace_list = []\n",
    "pde_model_train.load_weights('picn_initial_weights.h5')\n",
    "pde_model_train.compile(optimizer=keras.optimizers.Adam(learning_rate=0.01), loss=\"mse\", loss_weights = [0.001, 0.999])\n",
    "\n",
    "# Record the training time cost\n",
    "start = time.time()\n",
    "\n",
    "# Training\n",
    "pde_model_train.fit(x=training_input_data, \n",
    "                    y=training_label_data, \n",
    "                    epochs=5000, verbose=0,\n",
    "                    callbacks=callbacks)\n",
    "\n",
    "# Record the training time cost\n",
    "end = time.time()\n",
    "print(\"Training time cost: {:10.5f} seconds\".format(end - start))\n",
    "time_costs_text_file = open(\"time_costs.txt\", \"w\")\n",
    "time_costs_text_file.write(\"Training time cost: {:10.5f} seconds\".format(end - start))\n",
    "time_costs_text_file.close()\n",
    "\n",
    "# Load the best weights\n",
    "pde_model_train.load_weights('picn_best_weights.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df=pd.DataFrame({'epoch': epoch_number_list, \n",
    "                 'total_loss': total_loss_list, \n",
    "                 'governing_loss': domain_loss_list, \n",
    "                 'boundary_loss': boundary_loss_list})\n",
    "\n",
    "plt.figure(figsize=(12, 4))\n",
    "plt.plot( 'epoch', 'total_loss', data=df, marker='o', markerfacecolor='red', markersize=2, color='skyblue', linewidth=3)\n",
    "plt.plot( 'epoch', 'governing_loss', data=df, marker='', color='green', linewidth=2, linestyle='dashed')\n",
    "plt.plot( 'epoch', 'boundary_loss', data=df, marker='', color='blue', linewidth=2, linestyle='dashed')\n",
    "plt.legend()\n",
    "#plt.xscale('log')\n",
    "plt.yscale('log')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 11ms/step\n"
     ]
    }
   ],
   "source": [
    "[x_m, y_m, uxy, dudx, dudy, d2udx2, d2udy2, d2udxdy, laplace] = pde_model.predict(training_input_data[:3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 1., 1., ..., 1., 1., 0.],\n",
       "       [0., 1., 1., ..., 1., 1., 0.],\n",
       "       ...,\n",
       "       [0., 1., 1., ..., 1., 1., 0.],\n",
       "       [0., 1., 1., ..., 1., 1., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.]])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "governing_equation_mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'y')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x250 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "z_mesh = U(x_mesh,y_mesh)\n",
    "z_mesh[governing_equation_mask==0] = np.nan\n",
    "\n",
    "fig, axs = plt.subplots(1,3,constrained_layout=False, figsize=(16, 2.5))\n",
    "#\n",
    "ax = axs.ravel()[0]\n",
    "temp = uxy[0,:,:,0]\n",
    "temp[governing_equation_mask==0]=np.nan\n",
    "cs = ax.contourf(x_domain, y_domain, uxy[0,:,:,0])\n",
    "fig.colorbar(cs, ax=ax, shrink=0.9)\n",
    "ax.set_xlabel('x')\n",
    "ax.set_ylabel('y')\n",
    "#\n",
    "ax = axs.ravel()[1]\n",
    "cs = ax.contourf(x_domain, y_domain, z_mesh)\n",
    "fig.colorbar(cs, ax=ax, shrink=0.9)\n",
    "ax.set_xlabel('x')\n",
    "ax.set_ylabel('y')\n",
    "#\n",
    "ax = axs.ravel()[2]\n",
    "cs = ax.contourf(x_domain, y_domain, uxy[0,:,:,0]-z_mesh)\n",
    "fig.colorbar(cs, ax=ax, shrink=0.9)\n",
    "ax.set_xlabel('x')\n",
    "ax.set_ylabel('y')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8. Save the Process Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "picn_process_data = {'x_domain':x_domain,\n",
    "                     'y_domain':y_domain,\n",
    "                     'epoch_number_list':np.asarray(epoch_number_list),\n",
    "                     'total_loss_list':np.asarray(total_loss_list),\n",
    "                     'domain_loss_list':np.asarray(domain_loss_list),\n",
    "                     'boundary_loss_list':np.asarray(boundary_loss_list),\n",
    "                     'uxy_list':np.concatenate(uxy_list,axis=0),\n",
    "                     'dudx_list':np.concatenate(dudx_list,axis=0),\n",
    "                     'dudy_list':np.concatenate(dudy_list,axis=0),\n",
    "                     'd2udx2_list':np.concatenate(d2udx2_list,axis=0),\n",
    "                     'd2udy2_list':np.concatenate(d2udy2_list,axis=0),\n",
    "                     'd2udxdy_list':np.concatenate(d2udxdy_list,axis=0),\n",
    "                     'laplace_list':np.concatenate(laplace_list,axis=0)}\n",
    "savemat('picn_process_data.mat', picn_process_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
